A dynamic resilience management framework for deep-tier supply networks

Abstract

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

Keywords

• Supply network
• Resilience management
• Automotive industry
• Simulation-based optimization
• Surrogate model
• Secondary data